{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INST728E - Module 9. Topic Modeling\n",
    "\n",
    "Along with sentiment analysis, a question often asked of social networks is \"What are people talking about?\" We can answer this question using tools from topic modeling and natural language processing. With crises, people can have many responses, from sharing specific data about the event, sharing condolonces, or opening their homes to those in need.\n",
    "\n",
    "To generate these topic models, we will use the Gensim package's implementation of Latent Dirichlet Allocation (LDA), which basically constructs a set of topics where each topic is described as a probability distribution over the words in our tweets. Several other methods for topic modeling exist as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "import json\n",
    "import string\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import TweetTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Event Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crisisInfo = {\n",
    "    \"Women's March\": {\n",
    "        \"name\": \"Women's March 2017\",\n",
    "        \"time\": 1484992800, # 21 January 2017, 6:58 UTC to 08:11 UTC\n",
    "        \"directory\": \"womensmarch\",    # Where do we find the relevant files\n",
    "        \"keywords\": [    # How can we describe this event?\n",
    "            \"women's march\",\"resist\", \"notmypresident\",\"inauguration\",\"women's right\",\"human right\",\"planned parenthood\"\n",
    "        ],\n",
    "        \"place\": [\n",
    "            38.899539,# Latitude\n",
    "            -77.036551 # Longitude\n",
    "        ],\n",
    "        \"box\": {    # Where did this event occur?\n",
    "            \"lowerLeftLon\": -77.119759,\n",
    "            \"lowerLeftLat\": 38.791645,\n",
    "            \"upperRightLon\": -76.909393,\n",
    "            \"upperRightLat\": 38.995548,\n",
    "        }\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replace the name below with your selected crisis\n",
    "selectedCrisis = \"Women's March\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Reading Relevant Tweets\n",
    "\n",
    "Re-read our relevant tweets..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relevant Tweets: 4447\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "in_file_path = \"/Users/yutingliao/Desktop/INST728 E/relevant_tweet_output_keywords_updated.json\" # Replace this as necessary\n",
    "\n",
    "relevant_tweets = []\n",
    "with open(in_file_path, \"r\") as in_file:\n",
    "    for line in in_file:\n",
    "        relevant_tweets.append(json.loads(line.encode(\"utf8\")))\n",
    "        \n",
    "print(\"Relevant Tweets:\", len(relevant_tweets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Ordering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Time: 2017-01-21 10:01:00\n",
      "Stop Time: 2017-01-24 23:58:00\n",
      "Processed Times: 5158\n"
     ]
    }
   ],
   "source": [
    "# Twitter's time format, for parsing the created_at date\n",
    "timeFormat = \"%a %b %d %H:%M:%S +0000 %Y\"\n",
    "\n",
    "# Frequency map for tweet-times\n",
    "rel_frequency_map = {}\n",
    "for tweet in relevant_tweets:\n",
    "    # Parse time\n",
    "    currentTime = datetime.datetime.strptime(tweet['created_at'], timeFormat)\n",
    "\n",
    "    # Flatten this tweet's time\n",
    "    currentTime = currentTime.replace(second=0)\n",
    "\n",
    "    # If our frequency map already has this time, use it, otherwise add\n",
    "    extended_list = rel_frequency_map.get(currentTime, [])\n",
    "    extended_list.append(tweet)\n",
    "    rel_frequency_map[currentTime] = extended_list\n",
    "    \n",
    "# Fill in any gaps\n",
    "times = sorted(rel_frequency_map.keys())\n",
    "firstTime = times[0]\n",
    "lastTime = times[-1]\n",
    "thisTime = firstTime\n",
    "\n",
    "# We want to look at per-minute data, so we fill in any missing minutes\n",
    "timeIntervalStep = datetime.timedelta(0, 60)    # Time step in seconds\n",
    "while ( thisTime <= lastTime ):\n",
    "\n",
    "    rel_frequency_map[thisTime] = rel_frequency_map.get(thisTime, [])\n",
    "        \n",
    "    thisTime = thisTime + timeIntervalStep\n",
    "\n",
    "# Count the number of minutes\n",
    "print (\"Start Time:\", firstTime)\n",
    "print (\"Stop Time:\", lastTime)\n",
    "print (\"Processed Times:\", len(rel_frequency_map))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5MWaqMeYJY8x8Y8w+aVcMAAAA7TMgYrpfSZpmrT3aGDNI0iYp1gkAAABt1nCk0BizmaQP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\nPBF5V9s5Q/Oa36rrqpgzZq+CfISbWDZKzgq82Dlbr1tHtg0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LROQqEbnSP15MCSG8Igf3\nE+Vb68aKwDu+zBuGvvotxGsl3n6ReAeJyBIR+TsRudA/fl4NzhKcxwR39GClf/xpETm7arwQ3jQ5\nl5dzjoKI/F4bPFV9bPAB3VbO6Xi+tseJuy1jkVPehgx4c2bKi9V3z4nqtxnmTOK36Tip/VbmpPBb\nlkh9qDLGoHQiZ068JnPipiz4Gu6ci+8z9SeOwcUcHxjHqci7AHco/3W4c1aO9o+/CFxYhRczVhO8\nnHyU2m8hXmvIb0twJ9+fjTvv8bf84zXA2aEcz/suhZ+fCst3YOpPP6HxQrQF5eyLjyr47SzcuXFX\n4KYtKZ4PV/RHNF7Mvsf2W+ycffFRKCeF33IdE/PzsYiM+vlMgJ1UdXYqXkJtt+Hm1XtSRHbHTZOw\nXlX/SERWq+qLQjihsTxv6OSrIjK4KGH/UF7MWA1oM79NzRnbR8F+A35OVZ+YIkxkB9yV1vuHcPzz\ndbjzLu8q8fYFrlHVA0NzVtAWmvPWaXpwgKruGJuXImcFbbcBR6rqo+KOJF+Gu8jgwiE+isKL2Xf/\nPJrfGsjZFx+FxmrdbyN0Jcfs1AIq4CHcfEX/VV4hIpsS81Jpm62qTwKo6kMicgKwTEQ+i/tmGMqp\nwntcRI5Q1ZtL0g4HHq/IixkrNs/8NpUX20ehvC24K52nfMjhTtrfUoEDbl6560VkA25+SnAX2TwX\nN59jlZyhvNCc83ETkZfvEiO4Kyib4KXIGcqbpaqPAqjqnSJyLHCZ37mRwv/E5MXsO8T1W+ycffFR\naKwUfssSk7RTeCluwuJtPrhwRxlS8lJp+76IHKOqXwVQ1aeA00XkfbjpDUI5VXinAReJyK64qWLA\nzZj/sF9XhRczVmye+W0qL7aPQnkhH3JBH4SqukJEDgCOwJ2EL7j+r/T5q+QM4lXI+QXc1YprKEFE\nvtIQL0XOUN5mETl4wPFHXH4duBh4fuFfYvKi9d3niOm32Dn74qPQWCn8liUm5udjw7YQd/UWqvrY\nkHX7qOoPQzihsUrLFlDY6Kjq5hEax/JixmqCZwj3RxN+E5HtGPMhF8Kp+HqD4sXOa3AQkYW4e3AP\n214cpapfb4iXpO8pPG7YilR+yxG2U2gwGAwGg8Fg6MaUNAaDwWAwGAyGmcF2Cg0GQ28hInP7kNOQ\nB0TkxD7kNEwuOrFTKKXJIXPi9UmbwdAGQnaqhnFE5CgRWSsid4jIi0XkWuBbIrJJRI6cac5hvBQ5\nZ8JLkTOUlyhn0A7VMJ6InFwap+CusD9ZRE6ead4UOWeiLRdeztqygGYwWeJMBxMyQWZuvDqxcFdO\nfRN3BdwyYI/Cupur8GLGMm3dy+kfH4W7B+kdwIuBa4GN/v+ODOUM4vq8RwL/DRztlx8CfL1Kzgra\nWs/ZQN16oQ04uTROATYPnhdihfKexF39ejFb72v8iP97cZV4KXI2oK11Xs7ach0TMyWNiLx91Cpg\nTkpen7QBFwHn4j7Q3wjcKCInqur3ge0r8mLGMm3dywnwIeA1OA/+G3CSqt4oIocAH8Z92IdwwN3p\n4TYAEblfVW8EUNVVg6uhK+QM5aXIGbtufdH2GWAFcB88PZfcLsAJuHsIf84vC+UdCSzF3Wruo6qq\nInKsqpZvwRYSL0XO2NpS8HLWlidS75WGDtykwu8FzhkyHkrJ65m2NaW+/BKwAXgJU2/zM5YXM5Zp\n615Ov3x14fHa0v+sCuX4x7cUHp9U4t1eJWcFba3nbKBuvdCGm7z+euDNbJ2Z4wdFbhWeX74d8Dbg\ny7jpZDbWiZciZ2xtKXg5a8t1JBcQLNTNPn7oiHWbUvJ6pu0WYLfS+hfgPtAfqMKLGcu0dS/ngFd4\nPHSnKoTjH58I7DzE388B3lElZwVtredsoG590jZ2h6oKr8DfB3f0qHa8FDlja0vBy1lbjiO5gGCh\ncCCw54h181PyeqbtVOAlQziLgH+owosZy7R1L6d/PnanKoRTZYTGi5k3ds6YdeuTtsLyaXeoqvIq\n+GBsvBQ5Y2tLwctZW07DJq82GAy9gIjMwp2/uBBYoYW7CojIO1X1fV3IacgD5jfDJGJipqQRkVki\n8iYRea+IHFVa986UPNNm2rqurQv1AD4GHAM8APydiJxfWHdy4X9ivoYUOXvT05xzErH3OXvcfFSf\nlyMmZqeQQLMn4pk209Z1bV2oxxGqeqqqXoCblmSOiHxORHaEp68QjK0tRc5QXhd6mnPOmL3P2ePm\no/q8/JD69+vQAdxaeDwbN7fZ54AdmXplWes802bauq6tI/VYN2S78i7g68CGhrS1nrNnPc05Z7Te\np8iZc6+6oC3XMUlHCncYPFDVJ1X1DGANcANT59FLwTNtpq3r2rpQj2+JyOLCc1T1PbiJffdrSFuK\nnKG8LvQ055wxe5+zx81H9Xn5YaZ7lW0N4JPA4iHL3wg8kZJn2kxb17V1oR6hI/ZrSJGzLz3NOWfM\n3ufscfNR8x5pc9jVxwaDobcQkWXqvsV3OqchD5jfDLljkn4+3gYisixXnmmrxzNt9Xh9yRnKC40F\nHBYrZwVeipy96WnOOYnY+5w9bj6qz0uNid4pJNDsiXimrR7PtNXj9SVnKC801n0Rc4byUuQM5XWh\npznnjNn7nD1uPqrPS4pJ3ykMNXsKnmmrxzNt9Xh9yRnKC4qlqovHs8LjhfBS5KzAm/ie5pwzcu+z\n9XjkWLF5OWtLDjun0GAw9B52rpehTZjfDLli0o8UAnmfI2Da6vFMWz1eX3KG8oocEZk7YswDXtGE\nthQ5Y/Ampac552yr96k93las2LyctaXE7NQCQiEic0etomD2FDzTZtq6rq0L9QDuB+7yywdQ//xZ\nTWhLlLM3Pc05JxF7n7PHzUf1eTliYnYKCTR7Ip5pM21d19aFemwEjlPVuylBRDY1pC1FzlBeF3qa\nc86Yvc/Z4+aj+rz8kHqixNABbAAWjVi3KSXPtJm2rmvrSD3eCrxwBO/MhrS1nrNnPc05Z7TeZ+5x\n81FNXo5jks4pvADYY8S6DybmmTbT1nVtE18PVf17Vb1lGElVP9yEthQ5K/Amvqc554zc+2w9HjlW\nn7RlCbv62GAw9AYichDwSmAf3M859wJXqeraLuU05AHzm2HSMFE7haFmT8Ezbaat69omvR4isgR4\nPbAcuMcvXgi8Dliuqktja0uRM3bd+qQtcqyovc/V47Fj9UlbjpiYn4+92ZfjTtS8GVjpH39aRM5O\nyTNtpq3r2rpQD+B04HBVXaqqn/RjKXCEX9fEa0iRszc9zTknEXufs8fNRzPySH5IfVJj6AC+C2w/\nZPkOwIaUPNNm2rqurSP1WAfsO4S3L7C+IW2t5+xZT3POGa33mXvcfFSTl+OYmCOFwBZg7yHL9/Lr\nUvJMm2nrurYu1OMPgetF5GoRWebHCuB64G0NaUuRM5TXhZ7mnDNm73P2uPmoPi87TNI8hQOzbwAG\n8y0tAp4L/EFinmkzbV3XNvH1UNUVInIA7qe0fXA/59wDrFTVp5rQliJn7Lr1SFvUnJF7n63HY9et\nR9qyxKRdaLId482ehGfaTFvXtXWhHmWIyBmqus1tp2K/hhQ5+9LTnHOWMZPe5+xx81E8jyRH6t+v\nZzKAM3LlmTbT1nVtHanHqgTaWs/Zs57mnDNa7zP3uPmoJi/1SC5gRuLDzd46z7SZtq5r60g9VifQ\n1nrOnvU055zRep+5x81HNXmpxyRdaDIMkjHPtNXjmbZ6vL7kDOWFxjohYs5QXoqcobwu9DTnnDF7\nn7PHzUf1eWmReq90JgNYmCvPtJm2rmubtHoAZwHPDskRS1uKnH3qac45m+59Lh7PpVdd0JbDSC4g\nWGig2VPwTJtp67q2jtTjx7i7Cvw78BZgzxa0tZ6zZz3NOWe03mfucfNRTV6OI7mAYKHhZm+dZ9pM\nW9e1daQeq3F3cXo58HHgfmAF8LvArg1paz1nz3qac85ovc/c4+ajmrwcR3IBwULDzd46z7SZtq5r\n60g9ppzoDWwPnAh8Gri/IW2t5+xZT3POGa33KXLm3KsuaMt1JBcQLDTc7K3zTJtp67q2jtRj5FWY\nwE4NaWs9Z896mnPOaL3P3OPmo5q8HEdyAcFCw83eOs+0mbaua+tIPQ4YxSv9T0xtrefsWU9zzhmt\n95l73HxUk5fjSC4gWGi42VvnmTbT1nVtXajHmBhzmnoNKXL2pac554zZ+5w9bj5qpl+pxkTd5m4U\nRGSOqj6aI8+0mbaua+tIPe5W1UUta2s9ZwVtXehpzjmj9T5zj5uPavJSYXZqAZHwHdzNpnPkmbZ6\nPNNWj9eXnKG8pzki8vYRHAHmBOSrrC1Fzki8iehpzjlb7H1Sj7cYKzYvZ23JMDE7haFmT8Ezbaat\n69q6UA/g/cBfA08O4T59d6fIryFFzt70NOecROx9zh43H9Xn5YhJus3d+4E9gF1LYw5TX0cKnmkz\nbV3X1oV6rAKuUNV3lwfwSEPaUuSMXbe+aIudM2bvc/a4+ag+Lz+kPqkxdAA3AYeOWLcpJc+0mbau\na+tIPQ5k9GSz8xvS1nrOnvU055zRep+5x81HNXk5juQCgoWGm711nmkzbV3X1oV6hI7YryFFzr70\nNOecMXufs8fNR817pM3RiauPDQaDYRxEZDfgz4CTgD394vuAK4GlqvpQF3Ia8oD5zTCRSL1XGjqA\n3YClwDrgAT/W+mW7p+SZNtPWdW0dqceXgCXAgsKyBX7ZtQ1paz1nz3qac85ovU+RM+dedUFbriPv\nEx6n4jPAj4BjVXWeqs4Dfskv+2xinmkzbV3X1oV67Keq56nq5sECVd2squcxdYqImNpS5Ixdt75o\ni50zZu9z9rj5qD4vP5T3EnMdwPqQdSl4ps20dV1bR+pxDfAOpp77Mx93FOW6hrS1nrNnPc05Z7Te\nZ+5x81FNXo5jko4U3iUi7xCR+YMFIjJfRJYAmxLzTJtp67q2LtTjtcA84Ksi8qCIPAh8BZgLvKYh\nbSlyxq5bX7TFzhmz9zl73HxUn5cfUu+Vhg7cnD/n4X6jf9CPtX7Z3JQ802bauq6tC/VIta3JdfvW\nhZ7mnDNm71PkzLlXXdCW67Crjw0GQ28gIgcB+wDfVNX/KSxfrKorupLTkAfMb4aJQ+q90orfbg4C\njgN2KS1fnJpn2kxb17VNej2As4D1wBXAncArC+tWNaEtRc4+9TTnnLF7n6vHzUcz4+U2kgsIFhpo\n9hQ802bauq6tI/W4DZjjH+8HfAt4m3++uiFtrefsWU9zzhmt9yly5tyrLmjLdSQXECw03Oyt80yb\naeu6to7U4zulbcocYAVwPrCmIW2t5+xZT3POGa33KXLm3KsuaMt1zGZyMEtVHwVQ1TtF5FjgMhHZ\nF5DEPNNm2rqurQv12CwiB6vqGs99VER+HbgYeH5D2lLkjF23vmiLnTNm73P2uPmoPi8/VN2LTDWA\nG4CDS8tmA5cCT6XkmTbT1nVtHanHQgp3eijxj2pIW+s5e9bTnHNG633mHjcf1eTlOJILCBYabvbW\neabNtHVdWxfqETpiv4YUOfvS05xzxux9zh43HzXvkTaHTUljMBgMBoPBYJioO5oYDAaDwWAwGBqC\n7RQaDAaDwWAwGGyn0GAwGAwGg8FgO4UGg8FgMBgMBuD/AUGWFd8fRzrsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d394358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Tweet Frequencies\")\n",
    "\n",
    "sortedTimes = sorted(rel_frequency_map.keys())\n",
    "postFreqList = [len(rel_frequency_map[x]) for x in sortedTimes]\n",
    "\n",
    "smallerXTicks = range(0, len(sortedTimes), 90)\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "xData = range(len(sortedTimes))\n",
    "\n",
    "ax.plot(xData, postFreqList, color=\"blue\", label=\"Posts\")\n",
    "\n",
    "ax.grid(b=True, which=u'major')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr> \n",
    "\n",
    "## Hashtags as Topics\n",
    "\n",
    "Hashtags generally have a topical connotation, so let's regenerate the common hashtags we've seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total Hashtag Count: 2658\n",
      "Unique Hashtag Count: 783\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 533\n",
      "theresistance 285\n",
      "resist 115\n",
      "notmypresident 111\n",
      "resistance 74\n",
      "trumpleaks 50\n",
      "resisttrump 44\n",
      "trump 34\n",
      "whyimarch 30\n",
      "womensmarchonwashington 27\n",
      "resisttrumptuesdays 27\n",
      "inauguration 21\n",
      "lovatics 21\n",
      "breaking 20\n",
      "bestfanarmy 20\n",
      "alternativefacts 20\n",
      "indivisible 20\n",
      "iheartawards 19\n",
      "resistfromday1 14\n",
      "breakingnews 12\n"
     ]
    }
   ],
   "source": [
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the hashtags list\n",
    "htags = [\n",
    "        hashtag[\"text\"].lower() \n",
    "         for tweet in relevant_tweets \n",
    "             for hashtag in tweet[\"entities\"][\"hashtags\"]\n",
    "        ]\n",
    "\n",
    "print(\"\\nTotal Hashtag Count:\", len(htags))\n",
    "print(\"Unique Hashtag Count:\", len(set(htags)))\n",
    "\n",
    "htags_freq = nltk.FreqDist(htags)\n",
    "\n",
    "print(\"\\nFrequent Hashtags:\")\n",
    "for tag, count in htags_freq.most_common(20):\n",
    "    print(tag, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr> \n",
    "\n",
    "## Topic Modeling with Gensim\n",
    "\n",
    "A big part of topic modeling is pre-processing your data. \n",
    "\n",
    "For our context, that includes:\n",
    "\n",
    "- Which tokens are used so frequently as to be useless?\n",
    "- Which tokens are so rare as to be uninformative?\n",
    "- How should we handle phrases versus single words (called n-grams)?\n",
    "\n",
    "We'll explore this feature extraction below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Gotta pull in a bunch of packages for this\n",
    "\n",
    "# Actual LDA implementation\n",
    "import gensim.models.ldamulticore\n",
    "\n",
    "# Actual ATM implementation\n",
    "import gensim.models.atmodel\n",
    "\n",
    "# CountVectorizer turns tokens into numbers for us\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Gensim models\n",
    "from gensim.corpora import Dictionary  # All the words that appear in our dataset\n",
    "from gensim.models import TfidfModel # For down-weighting frequent tokens\n",
    "from gensim.models.phrases import Phrases # For building bigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we build a list of stop words using NLTK and other words we don't care about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# But first, read in stopwrods\n",
    "enStop = stopwords.words('english')\n",
    "frStop = stopwords.words('french')\n",
    "esStop = stopwords.words('spanish')\n",
    "\n",
    "# Skip stop words, retweet signs, @ symbols, and URL headers\n",
    "stopList = enStop +\\\n",
    "    frStop + esStop +\\\n",
    "    [\"http\", \"https\", \"rt\", \"@\", \":\", \"co\", \"amp\", \"&amp;\", \"...\", \"\\n\", \"\\r\"] +\\\n",
    "    crisisInfo[selectedCrisis][\"keywords\"]\n",
    "stopList.extend(string.punctuation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For memory/performance reasons, we don't want to carry around strings of characters when doing topic modeling. Instead, we can convert our tweets into a \"bag of words\" (BoW) model, in which a tweet is made up of words and their frequencies in that tweet. Then, for each word, we replace it with a specific indexed integer. The BoW model loses contextual information about which words occur before or after each other in the tweet, but we can address this with bigrams (to a degree).\n",
    "\n",
    "As an example, consider the following sets of tweets:\n",
    "\n",
    "1. \"my best friend lives in brussels and my friend isn’t responding\",\n",
    "1. \"Wish all but the best out to Brussels this morning\",\n",
    "1. \"So horrible. My thoughts are with the people in Brussels\",\n",
    "\n",
    "We can extract the following unique words from these tweets:\n",
    "{'.',\n",
    " 'all',\n",
    " 'and',\n",
    " 'are',\n",
    " 'best',\n",
    " 'brussels',\n",
    " 'but',\n",
    " 'friend',\n",
    " 'horrible',\n",
    " 'in',\n",
    " 'isn',\n",
    " 'lives',\n",
    " 'morning',\n",
    " 'my',\n",
    " 'out',\n",
    " 'people',\n",
    " 'responding',\n",
    " 'so',\n",
    " 't',\n",
    " 'the',\n",
    " 'this',\n",
    " 'thoughts',\n",
    " 'to',\n",
    " 'wish',\n",
    " 'with',\n",
    " '’'}\n",
    " \n",
    "From there, we can replace these tokens with indices: [(0, 'friend'),\n",
    " (1, 'lives'),\n",
    " (2, 'all'),\n",
    " (3, 'my'),\n",
    " (4, 'the'),\n",
    " (5, 'this'),\n",
    " (6, 'but'),\n",
    " (7, 'thoughts'),\n",
    " (8, 'best'),\n",
    " (9, 'and'),\n",
    " (10, 'are'),\n",
    " (11, 'so'),\n",
    " (12, '’'),\n",
    " (13, 't'),\n",
    " (14, 'in'),\n",
    " (15, '.'),\n",
    " (16, 'brussels'),\n",
    " (17, 'responding'),\n",
    " (18, 'wish'),\n",
    " (19, 'people'),\n",
    " (20, 'morning'),\n",
    " (21, 'with'),\n",
    " (22, 'out'),\n",
    " (23, 'to'),\n",
    " (24, 'isn'),\n",
    " (25, 'horrible')]\n",
    " \n",
    "Then we can convert tweets into the BoW model:\n",
    "\n",
    "1. \"my best friend lives in brussels and my friend isn’t responding\" --> [(0, 2), (1, 1), (3, 2), (8, 1), (9, 1), (12, 1), (14, 1), (16, 1), (17, 1), (24, 1)]\n",
    "    - \"my\" and \"friend\" occur twice, and their pairs (0, 2) and (3, 2) reflect this.\n",
    "1. \"Wish all but the best out to Brussels this morning\", --> [(2, 1), (4, 1), (5, 1), (6, 1), (8, 1), (16, 1), (18, 1), (20, 1), (22, 1), (23, 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RT @EvelynRBertrand: Women's March LA is already taking over Pershing Square. Here are some of the brilliant designs.… \n",
      "['@evelynrbertrand', \"women's\", 'march', 'already', 'taking', 'pershing', 'square', 'brilliant', 'designs']\n"
     ]
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(strip_accents='unicode', \n",
    "                             tokenizer=TweetTokenizer(preserve_case=False).tokenize,\n",
    "                             stop_words=stopList)\n",
    "\n",
    "# Build the Analyzer\n",
    "analyze = vectorizer.build_analyzer() \n",
    "\n",
    "# For each tweet, tokenize it according to the CountVectorizer\n",
    "analyzed_text = [analyze(tweet[\"text\"]) for tweet in relevant_tweets]\n",
    "\n",
    "# As an example, note the removed stopwords\n",
    "print(relevant_tweets[0][\"text\"])\n",
    "print(analyzed_text[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yutingliao/anaconda3/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RT @EvelynRBertrand: Women's March LA is already taking over Pershing Square. Here are some of the brilliant designs.… \n",
      "['@evelynrbertrand', \"women's\", 'march', 'already', 'taking', 'pershing', 'square', 'brilliant', 'designs']\n",
      "['@evelynrbertrand', \"women's_march\", 'already', 'taking', 'pershing', 'square', 'brilliant', 'designs']\n"
     ]
    }
   ],
   "source": [
    "# Make bigrams from the text, but only for really common bigrams\n",
    "bigram = Phrases(analyzed_text, min_count=5)\n",
    "bi_analyzed_text = [bigram[x] for x in analyzed_text]\n",
    "\n",
    "# As an example, note the removed stopwords\n",
    "print(relevant_tweets[0][\"text\"])\n",
    "print(analyzed_text[0])\n",
    "print(bi_analyzed_text[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Build a dictionary from this text\n",
    "dictionary = Dictionary(bi_analyzed_text)\n",
    "\n",
    "# Filter out words that occur too frequently or too rarely.\n",
    "# Disregarding stop words, this dataset has a very high number of low frequency words.\n",
    "max_freq = 0.75\n",
    "min_count = 5\n",
    "dictionary.filter_extremes(no_below=min_count, no_above=max_freq)\n",
    "\n",
    "# This sort of \"initializes\" dictionary.id2token.\n",
    "_ = dictionary[0]\n",
    "\n",
    "# Create a map for vectorizer IDs to words\n",
    "id2WordDict = dictionary.id2token\n",
    "word2IdDict = dict(map(lambda x: (x[1], x[0]), id2WordDict.items()))\n",
    "\n",
    "# Create a bag of words\n",
    "corpus = [dictionary.doc2bow(text) for text in analyzed_text]\n",
    "\n",
    "# Train TFIDF model\n",
    "tfidf_model = TfidfModel(corpus)\n",
    "\n",
    "# Built TFIDF-transformed corpus\n",
    "tfidf_corpus = [tfidf_model[text] for text in corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use the vectorizer to transform our tweet text into a feature set, which essentially is a table with rows of tweets, columns for each keyword, and each cell is the number of times that keyword appears in that tweet.\n",
    "\n",
    "We then convert that table into a model the Gensim package can handle, apply LDA, and grab the top 10 topics, 10 words that describe that topic, and print them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: march women's rights marches #theresistance could washington human planned today\n",
      "\n",
      "Topic 1: #theresistance #womensmarch rights #notmypresident march women's washington human absolutely people\n",
      "\n",
      "Topic 2: march women's rights #womensmarch washington today #notmypresident #resist trump human\n",
      "\n",
      "Topic 3: rights march women's black p change disabled education matter lives\n",
      "\n",
      "Topic 4: march vs trump's women's rights https://t.co/ylcmrdaxqi parenthood planned @mikefarrell #womensmarch\n",
      "\n",
      "Topic 5: rights trump women's marches march resistance #womensmarch women via world\n",
      "\n",
      "Topic 6: trump's vs #womensmarch march rights parenthood planned human women's next\n",
      "\n",
      "Topic 7: resistance rights march #womensmarch women's planned parenthood human 2017 dc\n",
      "\n",
      "Topic 8: march vs women's trump's washington rights resistance #theresistance #womensmarch ️\n",
      "\n",
      "Topic 9: march women's rights #womensmarch resistencia today washington human via women\n",
      "\n"
     ]
    }
   ],
   "source": [
    "k = 10\n",
    "\n",
    "lda = gensim.models.LdaMulticore(tfidf_corpus, \n",
    "                                 id2word=id2WordDict,\n",
    "                                 num_topics=k) # ++ iterations for better results\n",
    "\n",
    "ldaTopics = lda.show_topics(num_topics=k, \n",
    "                            num_words=10, \n",
    "                            formatted=False)\n",
    "\n",
    "for (i, tokenList) in ldaTopics:\n",
    "    print (\"Topic %d:\" % i, ' '.join([pair[0] for pair in tokenList]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualized Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim\n",
    "\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yutingliao/anaconda3/lib/python3.6/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
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       "\n",
       "\n",
       "<div id=\"ldavis_el238941122265530729148368158\"></div>\n",
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       "        new LDAvis(\"#\" + \"ldavis_el238941122265530729148368158\", ldavis_el238941122265530729148368158_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el238941122265530729148368158\", ldavis_el238941122265530729148368158_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "9      10.872345        1       1 -0.029478  0.033437\n",
       "3      10.761920        1       2  0.068220  0.002830\n",
       "6      10.558291        1       3  0.018250 -0.107080\n",
       "7      10.286598        1       4 -0.000587  0.005455\n",
       "1       9.902598        1       5 -0.081423  0.009071\n",
       "5       9.697603        1       6 -0.026314 -0.053698\n",
       "8       9.696780        1       7  0.019407  0.025724\n",
       "2       9.689439        1       8 -0.026587  0.035758\n",
       "4       9.570070        1       9  0.081717  0.034812\n",
       "0       8.964357        1      10 -0.023206  0.013690, topic_info=     Category        Freq                      Term       Total  loglift  \\\n",
       "term                                                                       \n",
       "406   Default   71.000000                        vs   71.000000  30.0000   \n",
       "238   Default   79.000000                   trump's   79.000000  29.0000   \n",
       "895   Default   14.000000   https://t.co/ylcmrdaxqi   14.000000  28.0000   \n",
       "855   Default   22.000000              @mikefarrell   22.000000  27.0000   \n",
       "632   Default   32.000000                 education   32.000000  26.0000   \n",
       "767   Default   40.000000           #notmypresident   40.000000  25.0000   \n",
       "474   Default   31.000000                         p   31.000000  24.0000   \n",
       "807   Default   34.000000                  disabled   34.000000  23.0000   \n",
       "1236  Default   26.000000               resistencia   26.000000  22.0000   \n",
       "510   Default   37.000000                     black   37.000000  21.0000   \n",
       "584   Default   34.000000                    change   34.000000  20.0000   \n",
       "391   Default   76.000000            #theresistance   76.000000  19.0000   \n",
       "1130  Default   32.000000                    matter   32.000000  18.0000   \n",
       "1004  Default   32.000000                     lives   32.000000  17.0000   \n",
       "999   Default   13.000000                absolutely   13.000000  16.0000   \n",
       "482   Default   16.000000   https://t.co/0svs9qe1dm   16.000000  15.0000   \n",
       "1483  Default   63.000000                parenthood   63.000000  14.0000   \n",
       "925   Default   11.000000                         ั   11.000000  13.0000   \n",
       "857   Default   12.000000   https://t.co/oqbkf6wxko   12.000000  12.0000   \n",
       "695   Default   18.000000                  congress   18.000000  11.0000   \n",
       "23    Default   17.000000                     could   17.000000  10.0000   \n",
       "28    Default   73.000000                washington   73.000000   9.0000   \n",
       "330   Default   24.000000                      next   24.000000   8.0000   \n",
       "85    Default   13.000000  #womensmarchonwashington   13.000000   7.0000   \n",
       "1274  Default    7.000000                  resistir    7.000000   6.0000   \n",
       "1587  Default   18.000000                screenings   18.000000   5.0000   \n",
       "44    Default   64.000000                   planned   64.000000   4.0000   \n",
       "362   Default   18.000000              https://t.co   18.000000   3.0000   \n",
       "43    Default  171.000000                     march  171.000000   2.0000   \n",
       "1513  Default    7.000000                     gover    7.000000   1.0000   \n",
       "...       ...         ...                       ...         ...      ...   \n",
       "377   Topic10    3.128824                  gathered   11.433874   1.1160   \n",
       "638   Topic10    8.364971                   marches   46.933000   0.6872   \n",
       "797   Topic10    4.912932                         u   23.803027   0.8340   \n",
       "1010  Topic10    3.556917                    trying   14.453354   1.0099   \n",
       "40    Topic10    4.285598                      city   20.991766   0.8230   \n",
       "669   Topic10    2.463008                       may    8.175648   1.2121   \n",
       "1087  Topic10    2.716625                      text    9.762742   1.1327   \n",
       "391   Topic10    7.885754            #theresistance   76.097484   0.1450   \n",
       "150   Topic10    3.663351                organizers   17.444491   0.8513   \n",
       "43    Topic10   11.510489                     march  171.199907  -0.2877   \n",
       "123   Topic10   10.093233                   women's  141.617448  -0.2294   \n",
       "250   Topic10    5.225303                        us   36.493419   0.4683   \n",
       "195   Topic10    4.356347                       get   25.060606   0.6623   \n",
       "122   Topic10    9.986378                    rights  153.377162  -0.3198   \n",
       "175   Topic10    4.239348                     woman   24.555987   0.6554   \n",
       "28    Topic10    6.467914                washington   73.196256  -0.0144   \n",
       "154   Topic10    4.505506                     right   31.021888   0.4825   \n",
       "20    Topic10    5.459679                     today   55.238265   0.0976   \n",
       "1287  Topic10    5.789427                     human   71.006216  -0.0948   \n",
       "44    Topic10    5.584328                   planned   64.891176  -0.0408   \n",
       "1483  Topic10    5.423764                parenthood   63.110918  -0.0422   \n",
       "515   Topic10    5.352058                resistance   75.910765  -0.2402   \n",
       "10    Topic10    5.284360                     trump   74.920047  -0.2398   \n",
       "252   Topic10    3.905541                   massive   22.204306   0.6740   \n",
       "34    Topic10    4.371226                    people   43.751850   0.1084   \n",
       "194   Topic10    3.810915                      news   21.765466   0.6695   \n",
       "177   Topic10    3.822047                   protest   24.405609   0.5579   \n",
       "21    Topic10    4.216261                     women   60.349399  -0.2493   \n",
       "58    Topic10    3.947918              #womensmarch  109.951310  -0.9149   \n",
       "32    Topic10    3.788506                      like   34.554676   0.2013   \n",
       "\n",
       "      logprob  \n",
       "term           \n",
       "406   30.0000  \n",
       "238   29.0000  \n",
       "895   28.0000  \n",
       "855   27.0000  \n",
       "632   26.0000  \n",
       "767   25.0000  \n",
       "474   24.0000  \n",
       "807   23.0000  \n",
       "1236  22.0000  \n",
       "510   21.0000  \n",
       "584   20.0000  \n",
       "391   19.0000  \n",
       "1130  18.0000  \n",
       "1004  17.0000  \n",
       "999   16.0000  \n",
       "482   15.0000  \n",
       "1483  14.0000  \n",
       "925   13.0000  \n",
       "857   12.0000  \n",
       "695   11.0000  \n",
       "23    10.0000  \n",
       "28     9.0000  \n",
       "330    8.0000  \n",
       "85     7.0000  \n",
       "1274   6.0000  \n",
       "1587   5.0000  \n",
       "44     4.0000  \n",
       "362    3.0000  \n",
       "43     2.0000  \n",
       "1513   1.0000  \n",
       "...       ...  \n",
       "377   -5.6357  \n",
       "638   -4.6523  \n",
       "797   -5.1845  \n",
       "1010  -5.5075  \n",
       "40    -5.3211  \n",
       "669   -5.8750  \n",
       "1087  -5.7770  \n",
       "391   -4.7113  \n",
       "150   -5.4780  \n",
       "43    -4.3331  \n",
       "123   -4.4645  \n",
       "250   -5.1229  \n",
       "195   -5.3047  \n",
       "122   -4.4751  \n",
       "175   -5.3320  \n",
       "28    -4.9095  \n",
       "154   -5.2711  \n",
       "20    -5.0790  \n",
       "1287  -5.0203  \n",
       "44    -5.0564  \n",
       "1483  -5.0856  \n",
       "515   -5.0989  \n",
       "10    -5.1116  \n",
       "252   -5.4140  \n",
       "34    -5.3013  \n",
       "194   -5.4385  \n",
       "177   -5.4356  \n",
       "21    -5.3374  \n",
       "58    -5.4032  \n",
       "32    -5.4444  \n",
       "\n",
       "[655 rows x 6 columns], token_table=      Topic      Freq             Term\n",
       "term                                  \n",
       "1002      3  0.358969    #iheartawards\n",
       "1002      4  0.089742    #iheartawards\n",
       "1002      5  0.089742    #iheartawards\n",
       "1002      7  0.269227    #iheartawards\n",
       "1002     10  0.089742    #iheartawards\n",
       "1410      6  0.323276          #nodapl\n",
       "1410      7  0.161638          #nodapl\n",
       "1410     10  0.323276          #nodapl\n",
       "767       1  0.024496  #notmypresident\n",
       "767       2  0.024496  #notmypresident\n",
       "767       3  0.073487  #notmypresident\n",
       "767       4  0.073487  #notmypresident\n",
       "767       5  0.367433  #notmypresident\n",
       "767       6  0.048991  #notmypresident\n",
       "767       7  0.024496  #notmypresident\n",
       "767       8  0.244956  #notmypresident\n",
       "767       9  0.048991  #notmypresident\n",
       "767      10  0.048991  #notmypresident\n",
       "732       1  0.081654          #resist\n",
       "732       2  0.081654          #resist\n",
       "732       3  0.054436          #resist\n",
       "732       4  0.081654          #resist\n",
       "732       5  0.081654          #resist\n",
       "732       6  0.081654          #resist\n",
       "732       7  0.136091          #resist\n",
       "732       8  0.244963          #resist\n",
       "732       9  0.054436          #resist\n",
       "732      10  0.081654          #resist\n",
       "1217      1  0.204895      #resistance\n",
       "1217      2  0.040979      #resistance\n",
       "...     ...       ...              ...\n",
       "451       3  0.058302                ✊\n",
       "451       5  0.116603                ✊\n",
       "451       6  0.116603                ✊\n",
       "451       7  0.233207                ✊\n",
       "451       9  0.116603                ✊\n",
       "451      10  0.058302                ✊\n",
       "786       1  0.152399                。\n",
       "786       4  0.457198                。\n",
       "786       7  0.152399                。\n",
       "786       8  0.152399                。\n",
       "786      10  0.152399                。\n",
       "170       1  0.086934                ️\n",
       "170       2  0.043467                ️\n",
       "170       3  0.043467                ️\n",
       "170       4  0.173867                ️\n",
       "170       5  0.130401                ️\n",
       "170       6  0.130401                ️\n",
       "170       7  0.347735                ️\n",
       "170       8  0.043467                ️\n",
       "646       2  0.067148                🏾\n",
       "646       3  0.201445                🏾\n",
       "646       5  0.067148                🏾\n",
       "646       6  0.335742                🏾\n",
       "646       7  0.134297                🏾\n",
       "646       8  0.067148                🏾\n",
       "646       9  0.067148                🏾\n",
       "646      10  0.067148                🏾\n",
       "754       1  0.102325                😍\n",
       "754       4  0.204651                😍\n",
       "754      10  0.511627                😍\n",
       "\n",
       "[2038 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[10, 4, 7, 8, 2, 6, 9, 3, 5, 1])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(lda, tfidf_corpus, dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Author-Topic Models\n",
    "\n",
    "Social media messages have an additional dimension we can leverage for identifying topics: their authors. If we make the assumption that, given all possible topics, a single user is only likely to tweet about a small number of them, then we can get some additional insight based on this data.\n",
    "\n",
    "We'll try this below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yutingliao/anaconda3/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    }
   ],
   "source": [
    "# Simple pipepline for analyzing tweet text\n",
    "def analysis_pipeline(text):\n",
    "    a1 = analyze(text)\n",
    "    a2 = bigram[a1]\n",
    "    a3 = dictionary.doc2bow(a2)\n",
    "    a4 = tfidf_model[a3]\n",
    "\n",
    "    return a4\n",
    "\n",
    "analyzed_tweet_pairs = list(\n",
    "    filter(lambda x: len(x[0]) > 0,\n",
    "           [(analysis_pipeline(tweet[\"text\"]), tweet[\"user\"][\"id\"]) \n",
    "            for tweet in relevant_tweets])\n",
    ")\n",
    "\n",
    "atm_docs = [x[0] for x in analyzed_tweet_pairs]\n",
    "doc_to_author = dict([(x, [y[1]]) for x, y in enumerate(analyzed_tweet_pairs)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: screenings breaking turned abortion women's fund bill texas away i'll\n",
      "\n",
      "Topic 1: screenings place give https://t.co/c9u5g0didn like body choice https://t.co/azbu5nc0af #istandwithpp walking\n",
      "\n",
      "Topic 2: screenings rights need office resistencia “ trump everything women's women\n",
      "\n",
      "Topic 3: screenings wo oh defund wait state trump government https://t.co/azbu5nc0af signs\n",
      "\n",
      "Topic 4: screenings order defund place https://t.co/azbu5nc0af gives https://t.c 100 https://t.co/c9u5g0didn abortions\n",
      "\n",
      "Topic 5: screenings p care ‘ ’ must americans already trump brown\n",
      "\n",
      "Topic 6: screenings defund thats https://t.co/azbu5nc0af #notmypresident sign rule footage https://t.co/c9u5g0didn we've\n",
      "\n",
      "Topic 7: screenings like get funding wo https://t.co/azbu5nc0af https://t.co/c9u5g0didn would god defund\n",
      "\n",
      "Topic 8: screenings https://t.co/azbu5nc0af fighting pro-life linda vs thing goes rights h\n",
      "\n",
      "Topic 9: screenings https://t.co/azbu5nc0af #theresistance @mashable washington else anyone https://t.co/c9u5g0didn standing every\n",
      "\n"
     ]
    }
   ],
   "source": [
    "k = 10\n",
    "\n",
    "atm = gensim.models.atmodel.AuthorTopicModel(corpus=atm_docs, \n",
    "                                             id2word=id2WordDict,\n",
    "                                             doc2author=doc_to_author,\n",
    "                                             num_topics=k) # ++ iterations for better results\n",
    "\n",
    "atmTopics = atm.show_topics(num_topics=k, \n",
    "                            num_words=10, \n",
    "                            formatted=False)\n",
    "\n",
    "for (i, tokenList) in atmTopics:\n",
    "    print (\"Topic %d:\" % i, ' '.join([pair[0] for pair in tokenList]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Per-Topic Times\n",
    "\n",
    "Now we can graph each topic over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "topic_counter = {x:[0]*len(rel_frequency_map) for x in range(lda.num_topics)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yutingliao/anaconda3/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    }
   ],
   "source": [
    "for (i, d) in enumerate(rel_frequency_map.keys()):\n",
    "    tweets = rel_frequency_map[d]\n",
    "    \n",
    "    for tweet in tweets:\n",
    "        text = tweet[\"text\"]\n",
    "        topic_dist = lda.get_document_topics(analysis_pipeline(text))\n",
    "        \n",
    "        top_topic = sorted(topic_dist, key=lambda x: x[1])[-1][0]\n",
    "        \n",
    "        topic_counter[top_topic][i] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sl5fHggULaGtrA6C4uNhfhG7dupXCwkLy8/OZM2dOn7a3bdvGlClTyMvLA+DC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\nhBBe/P8CKscm0EQs5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a236f7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Tweet Frequencies\")\n",
    "\n",
    "smallerXTicks = range(0, len(sortedTimes), 90)\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "xData = range(len(sortedTimes))\n",
    "\n",
    "for this_k in range(lda.num_topics):\n",
    "    plt.plot(xData, topic_counter[this_k], label=\"Topic %d\" % (this_k))\n",
    "\n",
    "ax.grid(b=True, which=u'major')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
